Overview

Dataset statistics

Number of variables12
Number of observations429
Missing cells230
Missing cells (%)4.5%
Duplicate rows2
Duplicate rows (%)0.5%
Total size in memory42.4 KiB
Average record size in memory101.3 B

Variable types

Categorical3
Text4
Numeric5

Dataset

Description경기도 부천시에 위치하고 있는 오피스텔 및 다세대, 다가구 주택 등 도시형 생활주택의 현황 정보로서 건물명, 위치, 용도 등의 정보를 제공합니다.
Author경기도 부천시
URLhttps://www.data.go.kr/data/15127144/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 2 (0.5%) duplicate rowsDuplicates
대지면적 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
건축면적 is highly overall correlated with 대지면적 and 2 other fieldsHigh correlation
지하층수 is highly overall correlated with 대지면적 and 2 other fieldsHigh correlation
지상층수 is highly overall correlated with 대지면적 and 2 other fieldsHigh correlation
주용도 is highly imbalanced (51.9%)Imbalance
건물명 has 23 (5.4%) missing valuesMissing
대지면적 has 8 (1.9%) missing valuesMissing
지하층수 has 7 (1.6%) missing valuesMissing
세대수 has 190 (44.3%) missing valuesMissing
지하층수 has 34 (7.9%) zerosZeros
세대수 has 49 (11.4%) zerosZeros

Reproduction

Analysis started2024-03-16 04:11:43.179459
Analysis finished2024-03-16 04:11:50.444743
Duration7.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
부천시 원미구
251 
부천시 소사구
149 
부천시 오정구
29 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부천시 원미구
2nd row부천시 원미구
3rd row부천시 원미구
4th row부천시 원미구
5th row부천시 원미구

Common Values

ValueCountFrequency (%)
부천시 원미구 251
58.5%
부천시 소사구 149
34.7%
부천시 오정구 29
 
6.8%

Length

2024-03-16T13:11:50.836855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:11:51.122971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부천시 429
50.0%
원미구 251
29.3%
소사구 149
 
17.4%
오정구 29
 
3.4%

건물명
Text

MISSING 

Distinct275
Distinct (%)67.7%
Missing23
Missing (%)5.4%
Memory size3.5 KiB
2024-03-16T13:11:51.580211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length6.6773399
Min length2

Characters and Unicode

Total characters2711
Distinct characters272
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201 ?
Unique (%)49.5%

Sample

1st row정다운 가
2nd row스타파크빌
3rd row아이원오피스텔
4th row트리플 타워 B
5th row제이클래스중동
ValueCountFrequency (%)
푸르지오 18
 
3.3%
시티 17
 
3.1%
신중동역 16
 
2.9%
랜드마크 15
 
2.7%
더 7
 
1.3%
제이클래스중동 6
 
1.1%
서영아너시티1차 6
 
1.1%
오피스텔 6
 
1.1%
보광팰리스 5
 
0.9%
위브더스테이트 5
 
0.9%
Other values (315) 446
81.5%
2024-03-16T13:11:52.541313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 178
 
6.6%
143
 
5.3%
리 106
 
3.9%
트 84
 
3.1%
이 79
 
2.9%
동 66
 
2.4%
오 54
 
2.0%
아 47
 
1.7%
티 47
 
1.7%
빌 46
 
1.7%
Other values (262) 1861
68.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2364
87.2%
Space Separator 143
 
5.3%
Uppercase Letter 115
 
4.2%
Decimal Number 43
 
1.6%
Lowercase Letter 22
 
0.8%
Letter Number 6
 
0.2%
Open Punctuation 6
 
0.2%
Close Punctuation 6
 
0.2%
Dash Punctuation 5
 
0.2%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 178
 
7.5%
리 106
 
4.5%
트 84
 
3.6%
이 79
 
3.3%
동 66
 
2.8%
오 54
 
2.3%
아 47
 
2.0%
티 47
 
2.0%
빌 46
 
1.9%
크 43
 
1.8%
Other values (220) 1614
68.3%
Uppercase Letter
ValueCountFrequency (%)
B 19
16.5%
A 18
15.7%
J 12
10.4%
N 10
8.7%
T 7
 
6.1%
C 7
 
6.1%
S 7
 
6.1%
H 6
 
5.2%
I 6
 
5.2%
L 4
 
3.5%
Other values (8) 19
16.5%
Lowercase Letter
ValueCountFrequency (%)
h 4
18.2%
l 3
13.6%
c 3
13.6%
y 3
13.6%
s 2
9.1%
e 2
9.1%
b 2
9.1%
a 1
 
4.5%
o 1
 
4.5%
z 1
 
4.5%
Decimal Number
ValueCountFrequency (%)
1 17
39.5%
2 9
20.9%
3 6
 
14.0%
4 3
 
7.0%
6 2
 
4.7%
5 2
 
4.7%
9 2
 
4.7%
8 2
 
4.7%
Space Separator
ValueCountFrequency (%)
143
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2364
87.2%
Common 204
 
7.5%
Latin 143
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 178
 
7.5%
리 106
 
4.5%
트 84
 
3.6%
이 79
 
3.3%
동 66
 
2.8%
오 54
 
2.3%
아 47
 
2.0%
티 47
 
2.0%
빌 46
 
1.9%
크 43
 
1.8%
Other values (220) 1614
68.3%
Latin
ValueCountFrequency (%)
B 19
13.3%
A 18
 
12.6%
J 12
 
8.4%
N 10
 
7.0%
T 7
 
4.9%
C 7
 
4.9%
S 7
 
4.9%
Ⅱ 6
 
4.2%
H 6
 
4.2%
I 6
 
4.2%
Other values (19) 45
31.5%
Common
ValueCountFrequency (%)
143
70.1%
1 17
 
8.3%
2 9
 
4.4%
3 6
 
2.9%
( 6
 
2.9%
) 6
 
2.9%
- 5
 
2.5%
4 3
 
1.5%
6 2
 
1.0%
5 2
 
1.0%
Other values (3) 5
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2364
87.2%
ASCII 341
 
12.6%
Number Forms 6
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 178
 
7.5%
리 106
 
4.5%
트 84
 
3.6%
이 79
 
3.3%
동 66
 
2.8%
오 54
 
2.3%
아 47
 
2.0%
티 47
 
2.0%
빌 46
 
1.9%
크 43
 
1.8%
Other values (220) 1614
68.3%
ASCII
ValueCountFrequency (%)
143
41.9%
B 19
 
5.6%
A 18
 
5.3%
1 17
 
5.0%
J 12
 
3.5%
N 10
 
2.9%
2 9
 
2.6%
T 7
 
2.1%
C 7
 
2.1%
S 7
 
2.1%
Other values (31) 92
27.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 6
100.0%

소재지주소
Text

Distinct303
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2024-03-16T13:11:53.021467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length21.006993
Min length17

Characters and Unicode

Total characters9012
Distinct characters70
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique235 ?
Unique (%)54.8%

Sample

1st row경기도 부천시 원미구 부일로 503
2nd row경기도 부천시 원미구 장말로 356-1
3rd row경기도 부천시 원미구 조마루로427번길 92
4th row경기도 부천시 원미구 신흥로 201
5th row경기도 부천시 원미구 석천로177번길 11
ValueCountFrequency (%)
경기도 429
20.0%
부천시 429
20.0%
원미구 251
 
11.7%
소사구 149
 
6.9%
경인로 34
 
1.6%
신흥로 32
 
1.5%
오정구 29
 
1.4%
길주로 24
 
1.1%
11 21
 
1.0%
223 16
 
0.7%
Other values (279) 731
34.1%
2024-03-16T13:11:53.981783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1716
19.0%
부 515
 
5.7%
경 499
 
5.5%
천 477
 
5.3%
구 429
 
4.8%
로 429
 
4.8%
기 429
 
4.8%
도 429
 
4.8%
시 429
 
4.8%
원 296
 
3.3%
Other values (60) 3364
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5662
62.8%
Space Separator 1716
 
19.0%
Decimal Number 1598
 
17.7%
Dash Punctuation 36
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 515
 
9.1%
경 499
 
8.8%
천 477
 
8.4%
구 429
 
7.6%
로 429
 
7.6%
기 429
 
7.6%
도 429
 
7.6%
시 429
 
7.6%
원 296
 
5.2%
미 276
 
4.9%
Other values (48) 1454
25.7%
Decimal Number
ValueCountFrequency (%)
2 288
18.0%
1 279
17.5%
7 174
10.9%
3 172
10.8%
4 150
9.4%
5 135
8.4%
8 113
 
7.1%
9 106
 
6.6%
6 101
 
6.3%
0 80
 
5.0%
Space Separator
ValueCountFrequency (%)
1716
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5662
62.8%
Common 3350
37.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 515
 
9.1%
경 499
 
8.8%
천 477
 
8.4%
구 429
 
7.6%
로 429
 
7.6%
기 429
 
7.6%
도 429
 
7.6%
시 429
 
7.6%
원 296
 
5.2%
미 276
 
4.9%
Other values (48) 1454
25.7%
Common
ValueCountFrequency (%)
1716
51.2%
2 288
 
8.6%
1 279
 
8.3%
7 174
 
5.2%
3 172
 
5.1%
4 150
 
4.5%
5 135
 
4.0%
8 113
 
3.4%
9 106
 
3.2%
6 101
 
3.0%
Other values (2) 116
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5662
62.8%
ASCII 3350
37.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1716
51.2%
2 288
 
8.6%
1 279
 
8.3%
7 174
 
5.2%
3 172
 
5.1%
4 150
 
4.5%
5 135
 
4.0%
8 113
 
3.4%
9 106
 
3.2%
6 101
 
3.0%
Other values (2) 116
 
3.5%
Hangul
ValueCountFrequency (%)
부 515
 
9.1%
경 499
 
8.8%
천 477
 
8.4%
구 429
 
7.6%
로 429
 
7.6%
기 429
 
7.6%
도 429
 
7.6%
시 429
 
7.6%
원 296
 
5.2%
미 276
 
4.9%
Other values (48) 1454
25.7%

대지면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct366
Distinct (%)86.9%
Missing8
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean11067.203
Minimum153.8
Maximum199287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-03-16T13:11:54.377355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum153.8
5-th percentile358.7
Q11389.1
median4262
Q311716
95-th percentile35257.3
Maximum199287
Range199133.2
Interquartile range (IQR)10326.9

Descriptive statistics

Standard deviation23811.47
Coefficient of variation (CV)2.1515345
Kurtosis33.91135
Mean11067.203
Median Absolute Deviation (MAD)3480.6
Skewness5.448769
Sum4659292.6
Variance5.6698608 × 108
MonotonicityNot monotonic
2024-03-16T13:11:54.863739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9008.3 12
 
2.8%
3261.2 10
 
2.3%
895.7 4
 
0.9%
685.1 3
 
0.7%
2497.3 3
 
0.7%
20718.0 3
 
0.7%
3120.5 3
 
0.7%
344.5 2
 
0.5%
2846.8 2
 
0.5%
10273.2 2
 
0.5%
Other values (356) 377
87.9%
(Missing) 8
 
1.9%
ValueCountFrequency (%)
153.8 2
0.5%
193.0 2
0.5%
197.5 1
0.2%
215.5 1
0.2%
234.9 1
0.2%
245.2 1
0.2%
264.6 1
0.2%
295.6 1
0.2%
297.1 1
0.2%
316.4 1
0.2%
ValueCountFrequency (%)
199287.0 1
0.2%
191291.1 1
0.2%
180166.0 1
0.2%
170362.5 1
0.2%
157177.6 1
0.2%
140002.2 1
0.2%
131943.0 1
0.2%
118827.0 1
0.2%
72454.0 1
0.2%
71746.4 2
0.5%

건축면적
Real number (ℝ)

HIGH CORRELATION 

Distinct370
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6879.8272
Minimum0
Maximum139028.34
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-03-16T13:11:55.137508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile229.248
Q1635.04
median2570.4
Q36334.9
95-th percentile20737.668
Maximum139028.34
Range139028.34
Interquartile range (IQR)5699.86

Descriptive statistics

Standard deviation15685.316
Coefficient of variation (CV)2.2798997
Kurtosis37.705949
Mean6879.8272
Median Absolute Deviation (MAD)2170.34
Skewness5.7519684
Sum2951445.8
Variance2.4602913 × 108
MonotonicityNot monotonic
2024-03-16T13:11:55.387754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6152.74 12
 
2.8%
457.0 5
 
1.2%
323.23 5
 
1.2%
228.98 4
 
0.9%
411.98 4
 
0.9%
9987.84 3
 
0.7%
2282.78 3
 
0.7%
478.63 3
 
0.7%
1799.08 3
 
0.7%
2570.4 3
 
0.7%
Other values (360) 384
89.5%
ValueCountFrequency (%)
0.0 2
0.5%
87.58 2
0.5%
137.06 2
0.5%
142.17 1
0.2%
145.32 1
0.2%
152.88 1
0.2%
157.7 1
0.2%
168.75 1
0.2%
187.92 1
0.2%
189.92 1
0.2%
ValueCountFrequency (%)
139028.34 1
0.2%
127086.63 1
0.2%
123054.88 1
0.2%
109942.8 1
0.2%
103962.56 1
0.2%
95829.06 1
0.2%
94968.3 1
0.2%
61233.12 1
0.2%
52546.83 1
0.2%
44223.9 1
0.2%

주용도
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
업무시설
308 
공동주택
114 
제2종근린생활시설
 
5
제1종근린생활시설
 
2

Length

Max length9
Median length4
Mean length4.0815851
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공동주택
2nd row공동주택
3rd row업무시설
4th row업무시설
5th row업무시설

Common Values

ValueCountFrequency (%)
업무시설 308
71.8%
공동주택 114
 
26.6%
제2종근린생활시설 5
 
1.2%
제1종근린생활시설 2
 
0.5%

Length

2024-03-16T13:11:55.909454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:11:57.061227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
업무시설 308
71.8%
공동주택 114
 
26.6%
제2종근린생활시설 5
 
1.2%
제1종근린생활시설 2
 
0.5%

세부용도
Text

Distinct154
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2024-03-16T13:11:58.008239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length31
Mean length15.340326
Min length3

Characters and Unicode

Total characters6581
Distinct characters85
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)20.5%

Sample

1st row공동주택,업무시설
2nd row공동주택 및 업무시설
3rd row업무시설
4th row근린생활시설,업무시설(오피스텔)
5th row업무시설, 근린생활시설
ValueCountFrequency (%)
업무시설 122
19.6%
업무시설(오피스텔 54
 
8.7%
공동주택 42
 
6.8%
및 41
 
6.6%
근린생활시설 41
 
6.6%
제1종근린생활시설 23
 
3.7%
업무시설(오피스텔,사무소),근린생활시설 18
 
2.9%
오피스텔 18
 
2.9%
제1종근린생활시설,업무시설 14
 
2.3%
공동주택,업무시설 12
 
1.9%
Other values (121) 236
38.0%
2024-03-16T13:11:59.150800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 661
 
10.0%
설 635
 
9.6%
, 407
 
6.2%
무 405
 
6.2%
업 381
 
5.8%
생 258
 
3.9%
활 246
 
3.7%
근 233
 
3.5%
린 221
 
3.4%
) 218
 
3.3%
Other values (75) 2916
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5287
80.3%
Other Punctuation 452
 
6.9%
Close Punctuation 221
 
3.4%
Open Punctuation 221
 
3.4%
Decimal Number 194
 
2.9%
Space Separator 192
 
2.9%
Dash Punctuation 12
 
0.2%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 661
 
12.5%
설 635
 
12.0%
무 405
 
7.7%
업 381
 
7.2%
생 258
 
4.9%
활 246
 
4.7%
근 233
 
4.4%
린 221
 
4.2%
주 206
 
3.9%
택 205
 
3.9%
Other values (61) 1836
34.7%
Other Punctuation
ValueCountFrequency (%)
, 407
90.0%
. 30
 
6.6%
/ 13
 
2.9%
· 2
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 218
98.6%
] 3
 
1.4%
Open Punctuation
ValueCountFrequency (%)
( 218
98.6%
[ 3
 
1.4%
Decimal Number
ValueCountFrequency (%)
1 118
60.8%
2 76
39.2%
Math Symbol
ValueCountFrequency (%)
> 1
50.0%
< 1
50.0%
Space Separator
ValueCountFrequency (%)
192
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5287
80.3%
Common 1294
 
19.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 661
 
12.5%
설 635
 
12.0%
무 405
 
7.7%
업 381
 
7.2%
생 258
 
4.9%
활 246
 
4.7%
근 233
 
4.4%
린 221
 
4.2%
주 206
 
3.9%
택 205
 
3.9%
Other values (61) 1836
34.7%
Common
ValueCountFrequency (%)
, 407
31.5%
) 218
16.8%
( 218
16.8%
192
14.8%
1 118
 
9.1%
2 76
 
5.9%
. 30
 
2.3%
/ 13
 
1.0%
- 12
 
0.9%
[ 3
 
0.2%
Other values (4) 7
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5286
80.3%
ASCII 1292
 
19.6%
None 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 661
 
12.5%
설 635
 
12.0%
무 405
 
7.7%
업 381
 
7.2%
생 258
 
4.9%
활 246
 
4.7%
근 233
 
4.4%
린 221
 
4.2%
주 206
 
3.9%
택 205
 
3.9%
Other values (60) 1835
34.7%
ASCII
ValueCountFrequency (%)
, 407
31.5%
) 218
16.9%
( 218
16.9%
192
14.9%
1 118
 
9.1%
2 76
 
5.9%
. 30
 
2.3%
/ 13
 
1.0%
- 12
 
0.9%
[ 3
 
0.2%
Other values (3) 5
 
0.4%
None
ValueCountFrequency (%)
· 2
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

지하층수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)1.9%
Missing7
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean2.7464455
Minimum0
Maximum7
Zeros34
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-03-16T13:11:59.454700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9000373
Coefficient of variation (CV)0.69181686
Kurtosis-0.50231893
Mean2.7464455
Median Absolute Deviation (MAD)1
Skewness0.63671199
Sum1159
Variance3.6101417
MonotonicityNot monotonic
2024-03-16T13:11:59.751222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 109
25.4%
1 89
20.7%
3 68
15.9%
5 46
10.7%
0 34
 
7.9%
4 29
 
6.8%
6 27
 
6.3%
7 20
 
4.7%
(Missing) 7
 
1.6%
ValueCountFrequency (%)
0 34
 
7.9%
1 89
20.7%
2 109
25.4%
3 68
15.9%
4 29
 
6.8%
5 46
10.7%
6 27
 
6.3%
7 20
 
4.7%
ValueCountFrequency (%)
7 20
 
4.7%
6 27
 
6.3%
5 46
10.7%
4 29
 
6.8%
3 68
15.9%
2 109
25.4%
1 89
20.7%
0 34
 
7.9%

지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)5.9%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean14.655738
Minimum3
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-03-16T13:12:00.051620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q110
median14
Q315
95-th percentile31.5
Maximum49
Range46
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.3272833
Coefficient of variation (CV)0.56819271
Kurtosis8.0480377
Mean14.655738
Median Absolute Deviation (MAD)4
Skewness2.583074
Sum6258
Variance69.343647
MonotonicityNot monotonic
2024-03-16T13:12:00.300748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
15 62
14.5%
14 53
12.4%
10 42
9.8%
13 36
 
8.4%
7 32
 
7.5%
9 24
 
5.6%
12 20
 
4.7%
11 19
 
4.4%
6 18
 
4.2%
20 17
 
4.0%
Other values (15) 104
24.2%
ValueCountFrequency (%)
3 1
 
0.2%
5 5
 
1.2%
6 18
4.2%
7 32
7.5%
8 12
 
2.8%
9 24
5.6%
10 42
9.8%
11 19
4.4%
12 20
4.7%
13 36
8.4%
ValueCountFrequency (%)
49 15
3.5%
35 3
 
0.7%
33 4
 
0.9%
28 1
 
0.2%
26 1
 
0.2%
25 2
 
0.5%
23 17
4.0%
21 1
 
0.2%
20 17
4.0%
19 15
3.5%

세대수
Real number (ℝ)

MISSING  ZEROS 

Distinct102
Distinct (%)42.7%
Missing190
Missing (%)44.3%
Infinite0
Infinite (%)0.0%
Mean189.98326
Minimum0
Maximum6960
Zeros49
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-03-16T13:12:00.586428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median42
Q3120
95-th percentile603
Maximum6960
Range6960
Interquartile range (IQR)112

Descriptive statistics

Standard deviation580.37423
Coefficient of variation (CV)3.0548703
Kurtosis87.426505
Mean189.98326
Median Absolute Deviation (MAD)42
Skewness8.4530155
Sum45406
Variance336834.24
MonotonicityNot monotonic
2024-03-16T13:12:00.883705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
11.4%
16 11
 
2.6%
8 11
 
2.6%
108 6
 
1.4%
20 6
 
1.4%
60 6
 
1.4%
112 5
 
1.2%
6 5
 
1.2%
28 5
 
1.2%
12 5
 
1.2%
Other values (92) 130
30.3%
(Missing) 190
44.3%
ValueCountFrequency (%)
0 49
11.4%
4 2
 
0.5%
6 5
 
1.2%
8 11
 
2.6%
12 5
 
1.2%
14 1
 
0.2%
15 1
 
0.2%
16 11
 
2.6%
18 3
 
0.7%
20 6
 
1.4%
ValueCountFrequency (%)
6960 1
0.2%
4194 1
0.2%
2175 1
0.2%
1547 1
0.2%
1280 1
0.2%
1232 1
0.2%
1156 1
0.2%
1043 1
0.2%
1040 1
0.2%
972 1
0.2%

층용도
Text

Distinct142
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2024-03-16T13:12:01.263281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length30
Mean length11.643357
Min length4

Characters and Unicode

Total characters4995
Distinct characters124
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)21.9%

Sample

1st row오피스텔
2nd row업무시설(오피스텔)
3rd row업무시설(오피스텔)-2호
4th row업무시설(오피스텔)
5th row자전거주차장(오피스텔)
ValueCountFrequency (%)
업무시설(오피스텔 91
19.4%
오피스텔 68
 
14.5%
업무시설(오피스텔-4호 21
 
4.5%
업무시설(오피스텔-2호 17
 
3.6%
업무시설(오피스텔-3호 14
 
3.0%
업무시설(오피스텔)-4호 14
 
3.0%
업무시설(오피스텔)-2호 13
 
2.8%
업무시설(오피스텔)-3호 7
 
1.5%
업무시설(오피스텔-5호 6
 
1.3%
업무시설(오피스텔)-5호 5
 
1.1%
Other values (138) 213
45.4%
2024-03-16T13:12:02.246265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 431
 
8.6%
피 430
 
8.6%
오 429
 
8.6%
텔 429
 
8.6%
( 389
 
7.8%
) 386
 
7.7%
시 308
 
6.2%
설 308
 
6.2%
무 306
 
6.1%
업 302
 
6.0%
Other values (114) 1277
25.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3610
72.3%
Open Punctuation 391
 
7.8%
Close Punctuation 388
 
7.8%
Decimal Number 281
 
5.6%
Dash Punctuation 178
 
3.6%
Other Punctuation 83
 
1.7%
Space Separator 40
 
0.8%
Uppercase Letter 23
 
0.5%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 431
11.9%
피 430
11.9%
오 429
11.9%
텔 429
11.9%
시 308
8.5%
설 308
8.5%
무 306
8.5%
업 302
8.4%
호 216
6.0%
실 58
 
1.6%
Other values (88) 393
10.9%
Decimal Number
ValueCountFrequency (%)
2 59
21.0%
4 53
18.9%
1 44
15.7%
3 40
14.2%
6 22
 
7.8%
5 17
 
6.0%
0 16
 
5.7%
8 16
 
5.7%
7 8
 
2.8%
9 6
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
E 10
43.5%
V 5
21.7%
L 5
21.7%
M 1
 
4.3%
D 1
 
4.3%
F 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 55
66.3%
: 24
28.9%
/ 4
 
4.8%
Open Punctuation
ValueCountFrequency (%)
( 389
99.5%
[ 2
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 386
99.5%
] 2
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 178
100.0%
Space Separator
ValueCountFrequency (%)
40
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3610
72.3%
Common 1362
 
27.3%
Latin 23
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 431
11.9%
피 430
11.9%
오 429
11.9%
텔 429
11.9%
시 308
8.5%
설 308
8.5%
무 306
8.5%
업 302
8.4%
호 216
6.0%
실 58
 
1.6%
Other values (88) 393
10.9%
Common
ValueCountFrequency (%)
( 389
28.6%
) 386
28.3%
- 178
13.1%
2 59
 
4.3%
, 55
 
4.0%
4 53
 
3.9%
1 44
 
3.2%
40
 
2.9%
3 40
 
2.9%
: 24
 
1.8%
Other values (10) 94
 
6.9%
Latin
ValueCountFrequency (%)
E 10
43.5%
V 5
21.7%
L 5
21.7%
M 1
 
4.3%
D 1
 
4.3%
F 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3610
72.3%
ASCII 1385
 
27.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 431
11.9%
피 430
11.9%
오 429
11.9%
텔 429
11.9%
시 308
8.5%
설 308
8.5%
무 306
8.5%
업 302
8.4%
호 216
6.0%
실 58
 
1.6%
Other values (88) 393
10.9%
ASCII
ValueCountFrequency (%)
( 389
28.1%
) 386
27.9%
- 178
12.9%
2 59
 
4.3%
, 55
 
4.0%
4 53
 
3.8%
1 44
 
3.2%
40
 
2.9%
3 40
 
2.9%
: 24
 
1.7%
Other values (16) 117
 
8.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2024-03-12
429 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-12
2nd row2024-03-12
3rd row2024-03-12
4th row2024-03-12
5th row2024-03-12

Common Values

ValueCountFrequency (%)
2024-03-12 429
100.0%

Length

2024-03-16T13:12:02.455795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:12:02.607026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-12 429
100.0%

Interactions

2024-03-16T13:11:46.440719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:43.823378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:44.438601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:45.197956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:45.874251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:46.555677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:43.915930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:44.615572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:45.338793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:46.000895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:46.682019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:44.015513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:44.782995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:45.493081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:46.126646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:46.835210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:44.168198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:44.943980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:45.623157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:46.237879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:46.973381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:44.300873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:45.080893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:45.743667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:11:46.349417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:12:02.704973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구대지면적건축면적주용도지하층수지상층수세대수
시군구1.0000.0000.0000.2510.3270.3470.000
대지면적0.0001.0000.9840.0000.3110.6160.735
건축면적0.0000.9841.0000.0000.3130.6300.743
주용도0.2510.0000.0001.0000.4800.4290.000
지하층수0.3270.3110.3130.4801.0000.7330.668
지상층수0.3470.6160.6300.4290.7331.0000.532
세대수0.0000.7350.7430.0000.6680.5321.000
2024-03-16T13:12:02.947077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주용도시군구
주용도1.0000.239
시군구0.2391.000
2024-03-16T13:12:03.126751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대지면적건축면적지하층수지상층수세대수시군구주용도
대지면적1.0000.9510.5560.5820.2940.0000.000
건축면적0.9511.0000.5540.5670.2890.0000.000
지하층수0.5560.5541.0000.6900.1080.2180.229
지상층수0.5820.5670.6901.0000.3740.2270.202
세대수0.2940.2890.1080.3741.0000.0000.000
시군구0.0000.0000.2180.2270.0001.0000.239
주용도0.0000.0000.2290.2020.0000.2391.000

Missing values

2024-03-16T13:11:47.246183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:11:48.625924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-16T13:11:49.773016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군구건물명소재지주소대지면적건축면적주용도세부용도지하층수지상층수세대수층용도데이터기준일자
0부천시 원미구정다운 가경기도 부천시 원미구 부일로 5031480.01108.48공동주택공동주택,업무시설310260오피스텔2024-03-12
1부천시 원미구스타파크빌경기도 부천시 원미구 장말로 356-1618.8354.48공동주택공동주택 및 업무시설<NA>716업무시설(오피스텔)2024-03-12
2부천시 원미구아이원오피스텔경기도 부천시 원미구 조마루로427번길 921467.6318.32업무시설업무시설29<NA>업무시설(오피스텔)-2호2024-03-12
3부천시 원미구트리플 타워 B경기도 부천시 원미구 신흥로 20114277.69987.84업무시설근린생활시설,업무시설(오피스텔)5150업무시설(오피스텔)2024-03-12
4부천시 원미구제이클래스중동경기도 부천시 원미구 석천로177번길 113120.52282.78업무시설업무시설, 근린생활시설619<NA>자전거주차장(오피스텔)2024-03-12
5부천시 원미구BH펠리스경기도 부천시 원미구 조마루로411번길 257333.64176.32업무시설업무시설(오피스텔),근린생활시설110<NA>업무시설(오피스텔-5호)2024-03-12
6부천시 원미구정담캐슬경기도 부천시 원미구 원미로 19510070.53472.04업무시설업무시설, 제1종근린생활시설212<NA>업무시설(오피스텔-4호)2024-03-12
7부천시 원미구트리플스테이트by로뎀경기도 부천시 원미구 원미로 24513365.03613.6업무시설업무시설, 제1종근린생활시설212<NA>업무시설(오피스텔-3호)2024-03-12
8부천시 원미구더뷰에디스경기도 부천시 원미구 부일로 5116964.15197.94업무시설업무시설, 공동주택217176업무시설(오피스텔)-4호2024-03-12
9부천시 원미구금광베네스타경기도 부천시 원미구 길주로 25238950.228973.16업무시설업무시설,제1.2종근린생활시설7204194오피스텔2024-03-12
시군구건물명소재지주소대지면적건축면적주용도세부용도지하층수지상층수세대수층용도데이터기준일자
419부천시 오정구길성그랑프리텔경기도 부천시 오정구 소사로748번길 29<NA>418.43공동주택공동주택(아파트),업무시설(오피스텔),근린생활시설21438계단실,승강기,홀(오피스텔용)2024-03-12
420부천시 오정구공간블리체경기도 부천시 오정구 소사로748번길 57-161700.71224.87공동주택아파트,업무시설(오피스텔)11260오피스텔(3호)2024-03-12
421부천시 오정구엘리시아경기도 부천시 오정구 원종로71번길 202183.01298.42공동주택공동주택,업무시설,제1종근린생활시설312120업무시설(오피스텔-3호)2024-03-12
422부천시 오정구원종스카이뷰경기도 부천시 오정구 원종로51번길 7396.7248.46공동주택공동주택(도시형생활주택),업무시설(오피스텔)1616업무시설(오피스텔)2024-03-12
423부천시 오정구원종센트레빌아파트경기도 부천시 오정구 원종로52번길 7-15846.0481.44공동주택공동주택(아파트),업무시설(오피스텔)21444업무시설(오피스텔)-4호2024-03-12
424부천시 오정구아이원팰리스경기도 부천시 오정구 원종로66번길 11-82542.5932.76공동주택공동주택(아파트),업무시설(오피스텔)21160오피스텔2024-03-12
425부천시 오정구DS타워경기도 부천시 오정구 여월로 667630.24569.6업무시설업무시설(오피스텔)37<NA>업무시설(오피스텔)2024-03-12
426부천시 오정구풍성애뜨란아파트경기도 부천시 오정구 원종로52번길 7-9795.0368.34공동주택공동주택(업무시설)1922오피스텔2024-03-12
427부천시 오정구세방오피스텔경기도 부천시 오정구 원종로52번길 21-11688.51034.4업무시설업무시설(오피스텔)160업무시설(오피스텔)2024-03-12
428부천시 오정구해윰아파트경기도 부천시 오정구 원종로79번길 13866.2514.74공동주택공동주택,업무시설,근린생활시설21147업무시설(오피스텔)-3호2024-03-12

Duplicate rows

Most frequently occurring

시군구건물명소재지주소대지면적건축면적주용도세부용도지하층수지상층수세대수층용도데이터기준일자# duplicates
0부천시 소사구하림골든뷰 2차경기도 부천시 소사구 경인옛로 5320718.02570.4업무시설업무시설(오피스텔),공동주택(도시형생활주택-단지형다세대)213108업무시설(오피스텔(4호))2024-03-123
1부천시 원미구신중동역 랜드마크 푸르지오 시티경기도 부천시 원미구 신흥로 2239008.36152.74업무시설업무시설(오피스텔,사무소),근린생활시설749<NA>주차장(오피스텔,근생)2024-03-122